Twenty-year impact of fire foci and its relationship with climate variables in Brazilian regions.

In recent years, Brazil has become a major global contributor to the occurrence of national fires and greenhouse gas emissions. Therefore, this study aimed to evaluate the fire foci data of the past 20 years to determine their relationship with climatic variables in various Brazilian regions. The variables evaluated included fire foci, land surface temperature, rainfall, and standardized precipitation index, which were obtained via remote sensing from 2000 to 2019. The data were subjected to trend analyses (Mann-Kendall and Pettitt tests) and a multivariate analysis of canonical variables for evaluation. The results showed that the Midwest and North regions had the highest occurrence of fire foci throughout the study period, and that the North region had the highest accumulated annual rainfall. Thus, these regions require specific public policies to prevent future fires. Overall, the Midwest, Southeast, and South regions exhibit significant increasing fire foci tendencies. Our results reveal that this trend is related to the El Niño-Southern Oscillation (ENSO) phenomena, which alter climatic variables such as precipitation, land surface temperature, and the standardized precipitation index. Finally, the sugarcane growing area had a significant linear relationship with fire foci in the Southeast region, especially in the state of São Paulo, the major national sugarcane producer.

Effect of land uses and land cover on soil attributes in the southern Brazilian Amazon.

The growth of the world population has led to the expansion of agricultural areas to produce food that meets world demand, making it necessary to increase productivity and maintain environmental sustainability in these areas. Seeking sustainable food production, the agricultural use of soil must be assessed in view of optimal use or land as natural resource, as well as minimize the effects of global warming related to land use and land cover (LULC). We hypothesize that different LULC affects Amazonian soil attributes. In this study, the effect of different LULC in the southern Brazilian Amazon, namely, native forest, pasture, and rice and soybean crops, on the spatial variability of soil fertility and texture was assessed, seeking to obtain information that will guide farmers in the near future to better exploit their areas and contribute to a more sustainable agriculture. Descriptive statistical analysis was performed for the pH, H + Al, Al, Ca, Mg, P, K, Cu, Fe, Mn, Zn, V, m, organic matter, clay, silt, and sand values from soil samples under different LULC. To verify the data normality, the Shapiro-Wilk test at 5% significance was performed. Outlier analysis using boxplot graphics, principal component analysis (PCA), and cluster analysis was performed. Data were submitted to geostatistical analysis to verify the spatial dependence degree of the variables through semivariograms for interpolated kriging maps. Except for silt, all variables were well represented in the factor map. PCA revealed that the data variability can be explained mainly by pH, V, Ca, K, and Zn values, which are inversely proportional to m, P, and sand. Through geostatistical analysis, spatial dependence ranging from moderate to strong was observed, generating reliability in the prediction of most attributes in pasture, rice, and soybean areas. Yet, a spatial dependence ranging from moderate to strong was found, generating reliability in the prediction of most attributes in pasture, rice, and soybean areas. Our findings reveal a lower fertility and higher acidity in forest areas, whereas crop areas presented the opposite result.

Application of remote sensing in environmental impact assessment: a case study of dam rupture in Brumadinho, Minas Gerais, Brazil.

The collapse of mining tailing dams in Brumadinho, Minas Gerais, Brazil, that occurred in 2019 was one of the worst environmental and social disasters witnessed in the country. In this sense, monitoring any impacted areas both before and after the disaster is crucial to understand the actual scenario and problems of disaster management and environmental impact assessment. In order to find answers to that problem, the aim of this study was to identify and analyze the spatiality of the impacted area by rupture of the tailing dam of the Córrego do Feijão mine in Brumadinho, Minas Gerais, by using orbital remote sensing. Land use and land occupation, phytoplankton chlorophyll-a, water turbidity, total suspended solids on water, and carbon sequestration efficiency by vegetation (CO2Flux) were estimated by orbital imagery from the Landsat-8/OLI and MSI/Sentinel-2 sensors in order to assess the environmental impacts generated by the disaster. Data were extracted from spectral models in which the variables that best demonstrated the land use variation over the years were sought. Mean comparison by t-test was performed to compare the time series analyzed, that is, before and after the disaster. Through the analysis of water quality, it was observed that the environmental impact was calamitous to natural resources, especially water from Córrego do Feijão.

High-throughput phenotyping using VIS/NIR spectroscopy in the classification of soybean genotypes for grain yield and industrial traits.

Employing visible and near infrared sensors in high-throughput phenotyping provides insight into the relationship between the spectral characteristics of the leaf and the content of grain properties, helping soybean breeders to direct their program towards improving grain traits according to researchers' interests. Our research hypothesis is that the leaf reflectance of soybean genotypes can be directly related to industrial grain traits such as protein and fiber contents. Thus, the objectives of the study were: (i) to classify soybean genotypes according to the grain yield and industrial traits; (ii) to identify the algorithm(s) with the highest accuracy for classifying genotypes using leaf reflectance as model input; (iii) to identify the best input data for the algorithms to improve their performance. A field experiment was carried out in randomized block design with three replications and 32 soybean genotypes. At 60 days after emergence, spectral analysis was carried out on three leaf samples from each plot. A hyperspectral sensor was used to capture reflectance between the wavelengths from 450 to 824 nm. Representative spectral bands were selected and grouped into means. After harvest, grain yield was assessed and laboratory analyses of industrial traits were carried out. Spectral, industrial traits and yield data were subjected to statistical analysis. Data were analyzed by the following machine learning algorithms: J48 (J48) and REPTree (DT) decision trees, Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and conventional Logistic Regression (LR) analysis. The clusters formed were used as the output of the models, while two groups of input data were used for the input of the models: the spectral variables (WL) noise-free obtained by the sensor (450-828 nm) and the spectral means of the selected bands (SB) (450.0-720.6 nm). Soybean genotypes were grouped according to their grain yield and industrial traits, in which the SVM and J48 algorithms performed better at classifying them. Using the spectral bands selected in the study improved the classification accuracy of the algorithms.

Machine learning in the classification of asian rust severity in soybean using hyperspectral sensor.

Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can increase efficiency in disease monitoring, enabling faster, more accurate and time- and labor-saving evaluations. The aims of the study were: (i) to identify the spectral signature of different levels of Asian soybean rust severity; (ii) to identify the most accurate machine learning algorithm for classifying disease severity levels; (iii) which spectral input provides the highest classification accuracy for the algorithms; (iv) to determine a sample size of leaves that guarantees the best accuracy for the algorithms. A field experiment was carried out in the 2022/2023 harvest in a randomized block design with a 6x3 factorial scheme (ML algorithms x severity levels) and four replications. Disease severity levels assessed were: healthy leaves, 25 % severity, and 50 % severity. Leaf hyperspectral analysis was carried out over a wide range from 350 to 2500 nm. From this analysis, 28 spectral bands were extracted, seeking to distinguish the spectral signature for each severity level with the least input dataset. Data was subjected to machine learning analysis using Artificial Neural Network (ANN), REPTree (DT) and J48 decision trees, Random Forest (RF), and Support Vector Machine (SVM) algorithms, as well as a traditional classification method (Logistic Regression - LR). Two different input datasets were tested for each algorithm: the full spectrum (ALL) provided by the sensor and the 28 spectral bands (SB). Tests with different sample sizes were also conducted to investigate the algorithms' ability to detect severity levels with a reduced sample size. Our findings indicate differences between the spectral curves for the severity levels assessed, which makes it possible to differentiate between healthy plants with low and high severity using hyperspectral sensing. SVM was the most accurate algorithm for classifying severity levels by using all the spectral information as input. This algorithm also provided high classification accuracy when using smaller leaf samples. This study reveals that hyperspectral sensing and the use of ML algorithms provide an accurate classification of different levels of Asian rust severity, and can be powerful tools for a more efficient disease monitoring process.

Flavonoids and their relationship with the physiological quality of seeds from different soybean genotypes.

Flavonoids are compounds that result from the secondary metabolism of plants and play a crucial role in plant development and mitigating biotic and abiotic stresses. The highest levels of flavonoids are found in legumes such as soybean. Breeding programs aim to increase desirable traits, such as higher flavonoid contents and vigorous seeds. Soybeans are one of the richest sources of protein in the plant kingdom and the main source of flavonoid derivatives for human health. In view of this, the hypothesis of this study is based on the possibility that the concentration of isoflavones in soybean seeds contributes to the physiological quality of the seeds. The aim of this study was to analyze the content of flavonoids in soybean genotypes and their influence on the physiological quality of the seeds. Seeds from thirty-two soybean genotypes were obtained by carrying out a field experiment during the 2021/22 crop season. The experimental design was randomized blocks with four replications and thirty-two F3 soybean populations. The seeds obtained were subjected to germination, first germination counting, electrical conductivity and tetrazolium vigor and viability tests. After drying and milling the material from each genotype, liquid chromatography analysis was carried out to obtain flavonoids, performed at UPLC level. Data were submitted to analysis of variance and, when significant, the means were compared using the Scott-Knott test at 5% probability. The results found here show the occurrence of genotypes with higher amounts of flavonoids when compared to their peers. The flavonoid FLVD_G2 had the highest concentration and differed from the others. Thus, we can assume that the type and concentration of flavonoids does not influence the physiological quality of seeds from different soybean genotypes, but it does indirectly contribute to viability and vigor, since the genotypes with the highest FLVD_G2 levels had better FGC values. The findings indicate that there is a difference between the content of flavonoids in soybean genotypes, with a higher content of genistein. The content of flavonoids does not influence the physiological quality of seeds, but contributes to increasing viability and vigor.

Reduction of pesticide application via real-time precision spraying.

Farmers focus on reducing the cost of production and aim to increase profit. The objective of this study was to quantify the reduction of pesticides applied to soybean (Glycine max (L.) Merrill) and maize (Zea mays L.) crops in several stages of the production cycle using a site-specific spraying application based on real-time sensors in the Brazilian Cerrado region. The sprayers were equipped with a precision spraying control system based on a real-time sensor. The spraying operations were performed not only for herbicide, but also for fungicide and insecticides applications. The maps recorded the percentage of the spray boom when the application was turned on (on/off spray system) with nozzle-to-nozzle control. The precision spraying system based on real-time sensors reduced the volume of pesticides (including herbicides, insecticides, and fungicides) applied to soybean and maize crops. There was a more significant reduction in the volume of pesticides applied post-emergence of the crops in the initial stages of soybean and maize when the crops had less leaf area or less foliage coverage between the rows. The cost reduction achieved using this technology was 2.3 times lower than the cost associated with pesticide application over the entire area using a conventional sprayer. Under the experimental conditions, there were no differences in the average crop yield, compared to the historical productivity of soybean and maize crops by applying this technology because the recommended doses were not affected and the site of application was limited to points where the presence of plants was present was detected.

High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield.

BACKGROUND: Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements. RESULTS: In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE. CONCLUSIONS: Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs.

Variable-rate in corn sowing for maximizing grain yield.

Sowing density is one of the most influential factors affecting corn yield. Here, we tested the hypothesis that, according to soil attributes, maximum corn productivity can be attained by varying the seed population. Specifically, our objectives were to identify the soil attributes that affect grain yield, in order to generate a model to define the optimum sowing rate as a function of the attributes identified, and determine which vegetative growth indices can be used to predict yield most accurately. The experiment was conducted in Chapadão do Céu-GO in 2018 and 2019 at two different locations. Corn was sown as the second crop after the soybean harvest. The hybrids used were AG 8700 PRO3 and FS 401 PW, which have similar characteristics and an average 135-day cropping cycle. Tested sowing rates were 50, 55, 60, and 65 thousand seeds ha-1. Soil attributes evaluated included pH, calcium, magnesium, phosphorus, potassium, organic matter, clay content, cation exchange capacity, and base saturation. Additionally, we measured the correlation between the different vegetative growth indices and yield. Linear correlations were obtained through Pearson's correlation network, followed by path analysis for the selection of cause and effect variables, which formed the decision trees to estimate yield and seeding density. Magnesium and apparent electrical conductivity (ECa) were the most important soil attributes for determining sowing density. Thus, the plant population should be 56,000 plants ha-1 to attain maximum yield at ECa values > 7.44 mS m-1. In addition, the plant population should be 64,800 plants ha-1 at values < 7.44 mS m-1 when magnesium levels are greater than 0.13 g kg-1, and 57,210 plants ha-1 when magnesium content is lower. Trial validation showed that the decision tree effectively predicted optimum plant population under the local experimental conditions, where yield did not significantly differ among populations.

Persistent fire foci in all biomes undermine the Paris Agreement in Brazil.

Brazil is one of the world's biggest emitters of greenhouse gases (GHGs). Fire foci across the country contributes to these emissions and compromises emission reduction targets pledged by Brazil under the Paris Agreement. In this paper, we quantify fire foci, burned areas, and carbon emissions in all Brazilian biomes (i.e., Amazon, Cerrado, Caatinga, Atlantic Forest, Pantanal and Pampa). We analyzed these variables using cluster analysis and non-parametric statistics to predict carbon and CO2 emissions for the next decade. Our results showed no increase in the number of fire foci and carbon emissions for the evaluated time series, whereby the highest emissions occur and will persist in the Amazon and Cerrado biomes. The Atlantic Forest, Pantanal, Caatinga and Pampa biomes had low emissions compared to the Amazon and Cerrado. Based on 2030 projections, the sum of emissions from fire foci in the six Brazilian biomes will exceed 5.7 Gt CO2, compromising the national GHG reduction targets. To reduce GHG emissions, Brazil will need to control deforestation induced by the expansion of the agricultural frontier in the Amazon and Cerrado biomes. This can only be achieved through significant political effort involving the government, entrepreneurs and society as a collective.

Factors affecting aerial spray drift in the Brazilian Cerrado.

Pesticides aerial application may results in the drift to neighboring areas if some application technology is not well executed. This phenomenon should be minimized to reduce environmental risks and agricultural production costs. This work aimed to investigate the interaction of environmental conditions, surrounding distance, and application conditions influencing spray drift in aerial applications. Sampling data from aerial sprays were collected during three agricultural years (from 2012 to 2014) in fields cultivated with sorghum, millet, soybean, corn, and cotton. The following variables were evaluated: application swath width, application rate, distance from the applied field, wind speed, relative humidity, and temperature. The estimated Pearson's correlations and path analysis identified that application rate and distance from the applied field and application were the variables that most influenced drift. Equations relating spray drift in function of distance from the applied field and application rate were adjusted in function of the variable, and a response surface model was constructed to estimate drift. The major pesticide class sprayed with aircraft in the Brazilian Cerrado was insecticide, followed by fungicide. This scenario shows the potential hazard risk of spray drift over the environment. The concentration of the drift deposits decreased as surrounding distance and application rate were increased. A mathematical equation of drift prediction was established, where the variables that contributed most to drift deposits were surrounding distance and wind speed. Thus, it is very important to monitor and respect the wind speed limits during the aerial spraying, mainly when there is any risk potential associated with pesticide exposure over the downwind direction.

Correction: Factors affecting aerial spray drift in the Brazilian Cerrado.

[This corrects the article DOI: 10.1371/journal.pone.0212289.].

Understanding the combining ability for physiological traits in soybean.

Photosynthetic efficiency has become the target of several breeding programs since the positive correlation between photosynthetic rate and yield in soybean suggests that the improvement of photosynthetic efficiency may be a promising target for new yield gains. However, studies on combining ability of soybean genotypes for physiological traits are still scarce in the literature. The objective of this study was to estimate the combining ability of soybean genotypes based on F2 generation aiming to identify superior parents and segregating populations for physiological traits. Twenty-eight F2 populations resulting from partial diallel crossings between eleven lines were evaluated in two crop seasons for the physiological traits: photosynthesis, stomatal conductance, internal CO2 concentration, and transpiration. General combining ability (GCA) of the parents and specific combining ability (SCA) of the F2 populations were estimated. Our findings reveal the predominance of additive effects in controlling the traits. The genotype TMG 7062 IPRO is the most promising parent for programs aiming at photosynthetic efficiency. We have also identified other promising parents and proposed cross-breeding with higher potential for obtaining superior lines for photosynthetic efficiency.

Developing an algorithm to geographically estimate the available time for agricultural field spraying in the state of Mato Grosso do Sul / Desenvolvendo um algoritmo para estimar geograficamente o tempo disponível para pulverização agrícola no estado de Mato Grosso do Su

The dimensions of mechanized agricultural systems depend on the edaphoclimatic conditions, crops, and work regimes. This study aimed to geographically estimate the monthly available time and number of favorable hours for agricultural field spraying in the state of Mato Grosso do Sul, Brazil. The meteorological restrictions imposed during unfavorable hours were as follows: ambient temperature above 32 ºC, relative humidity below 50 %, wind speed above 15 km h-1, and volumetric soil humidity above 39 % (equivalent to 90 % of the available water capacity). Mathematical models were then developed considering a period of ten years, which used historical data from the ground monitoring stations of the National Institute of Meteorology within the region. The subsequent algorithm was programmed and installed in a web server to simulate the time required for agricultural field spraying. During the cropping period in the region, there were climatic restrictions on performing agricultural spraying, with relative humidity being the variable with the most significant impact. However, soil moisture conditions restricted the available time for agricultural spraying more than the wind speed, relative air humidity, or ambient temperature.

O dimensionamento de sistemas mecanizados agrícolas depende das condições edafoclimáticas, da cultura e do regime de trabalho. O objetivo deste trabalho foi estimar geograficamente o tempo disponível mensal e o número de horas favoráveis à pulverização agrícola no Estado de Mato Grosso do Sul, Brasil. As restrições meteorológicas impostas para o cálculo das horas desfavoráveis foram: temperatura ambiente acima de 32ºC, umidade relativa abaixo de 50%, velocidade do vento acima de 15 km h-1 e umidade volumétrica do solo acima de 39% (equivalente a 90% da capacidade hídrica disponível). Os modelos matemáticos foram elaborados considerando um período de dez anos, com base nos dados históricos das estações automáticas do Instituto Nacional de Meteorologia instaladas no Estado. O algoritmo desenvolvido e instalado em ambiente web permitiu simular o tempo disponível para pulverização agrícola no estado de Mato Grosso do Sul. Durante o período de safra na região, há restrições climáticas para a realização da atividade de pulverização agrícola, sendo a umidade relativa do ar a variável de maior impacto. A condição de umidade do solo é mais restritiva para o tempo disponível para a pulverização agrícola do que a velocidade do vento, umidade relativa do ar ou temperatura do ambiente.

Relationship between vegetation indices and agronomic performance of maize varieties under different nitrogen rates / Relação entre índices de vegetação e desempenho agronômico de variedades de milho sob diferentes doses de nitrogênio

Precision agriculture is a set of techniques that assist the monitoring of the agronomic performance of the maize crop by using vegetation indices. This study aimed to verify the relationship between vegetation indices, plant height, leaf N content, and grain yield of three maize varieties, grown under high and low N as topdressing. The experiment was carried out at the Fundação de Apoio à Pesquisa Agropecuária de Chapadão (Fundação Chapadão), located in the municipality of Chapadão do Sul, during the 2017/2018 season. The experiment consisted of a randomized block design with four replications, arranged in a 3x2 split-plot scheme. The first factor (plots) corresponded to three open-pollinated maize varieties (BRS 4103, BRS Gorotuba, and SCS 154), and the second factor (subplots) consisted of two N rates applied as topdressing (80 and 160 kg- 1). All the evaluated variables showed varieties x N interaction. Vegetation indices in maize varieties were influenced by the N rate applied as topdressing. Normalized Difference Vegetation Index (NDVI) and Soil-adjusted Vegetation Index (SAVI) showed a higher correlation with plant height. At the same time, Normalized Difference Red Edge (NDRE) had a stronger association with leaf N content.

A agricultura de precisão é um conjunto de técnicas que auxiliam no monitoramento do desempenho agronômico da cultura do milho utilizando índices de vegetação. Este trabalho teve como objetivo verificar a relação entre índices de vegetação, altura de planta, teor de N foliar e rendimento de grãos de três variedades de milho, cultivadas sob alto e baixo N, em cobertura. O experimento foi realizado na Fundação de Apoio à Pesquisa Agropecuária de Chapadão, localizada no município de Chapadão do Sul, na safra 2017/2018. O experimento consistiu de um delineamento de blocos casualizados com quatro repetições, dispostos em esquema de parcelas subdivididas 3x2. O primeiro fator (parcelas) correspondeu a três variedades de milho de polinização aberta (BRS 4103, BRS Gorotuba e SCS 154), e o segundo fator (subparcelas) consistiu de duas doses de N aplicadas como cobertura (80 e 160 kg-1). Todas as variáveis avaliadas apresentaram interação variedades x N. Os índices de vegetação nas variedades de milho foram influenciados pela dose de N aplicada como cobertura. Os índices NDVI e SAVI mostraram uma maior correlação com a altura da planta, enquanto o NDRE apresentou uma associação mais forte com o conteúdo de N foliar.

Liquid nitrogen fertilization on the yield and phenologic variables of narrow-row cotton / Adubação nitrogenada líquida na produtividade e variáveis fenológicas em algodoeiro adensado

Slow-release liquid nitrogen fertilizer sources have been around since 1970. However, this technique is not widely used in the field, probably due to the low cost per ton of nitrogen in the solid form. This study aimed to evaluate the combination of the conventional and slow-release nitrogen fertilization on the yield and phenological variables in a narrow-row cotton crop. Treatments consisted of the combination of the nitrogen fertilizer applied as topdressing in solid form (via soil) and pulverized in the liquid form (via urea formaldehyde - UF): 0% of solid N + 0% of liquid N (0S0L); 100% of solid N + 0% of liquid N (100S0L); 75% of solid N + 25% of liquid N (75S25L); 50% of solid N + 50% of liquid N (50S50L); 25% of solid N + 75% of liquid N (25S75L); e 0% of solid N + 100% of liquid N (0S100L). The cotton crop was phenotypically evaluated at 35, 70, and 130 DAE (Days after emergence) and at the harvest time. The variation between the source of the solid nitrogen fertilizer applied to the soil (ammonium nitrate) and the liquid Nitrogen fertilizer applied by spraying (UF) affects the phenology, physiology, and yield components of the narrow-row cotton crop. The highest yield of the narrow-row cotton crop under the experimental conditions was achieved when 25% of the dose of the solid nitrogen fertilizer was applied as topdressing via soil, and 75% of the dose of the slow-release nitrogen fertilizer was sprayed. This technique provides higher profitability of the produced cotton in relation to the conventional application of the N solid fertilizer via soil.

Fontes de fertilizantes nitrogenados líquidos de liberação lenta existem desde 1970. No entanto, esta técnica não é amplamente utilizada no campo, provavelmente devido ao baixo custo por tonelada de nitrogênio na forma sólida. Este trabalho teve como objetivo avaliar a combinação da adubação nitrogenada convencional e de liberação lenta sobre a produtividade e as variáveis fenológicas em uma cultura de algodão de fileira estreita. Os tratamentos consistiram da combinação do fertilizante nitrogenado aplicado como cobertura na forma sólida (via solo) e pulverizado na forma líquida (via uréia formaldeído - UF): 0% de N sólido + 0% de N líquido (0S0L); 100% de N sólido + 0% de N líquido (100 S); 75% de N sólido + 25% de N líquido (75S25L); 50% de N sólido + 50% de N líquido (50S50L); 25% de N sólido + 75% de N líquido (25S75L); e 0% de N sólido + 100% de N líquido (0S100L). A cultura do algodão foi avaliada fenotipicamente aos 35, 70 e 130 DAE (dias após a emergência) e no momento da colheita. A variação entre a fonte de adubação nitrogenada aplicada ao solo (nitrato de amônio) e o fertilizante nitrogenado líquido aplicado por pulverização (UF) afeta os componentes fenológicos, fisiológicos e produtivos da cultura de algodão de fileiras estreitas. O maior rendimento da cultura de algodão de linha estreita nas condições experimentais foi alcançado quando 25% da dose do fertilizante de nitrogênio sólido foi aplicado como cobertura de solo via solo, e 75% da dose do fertilizante de nitrogênio de liberação lenta foi pulverizada. Esta técnica proporciona maior rentabilidade do algodão produzido em relação à aplicação convencional do fertilizante N via solo.

In situ remote sensing as a strategy to predict cotton seed yield / Sensoriamento remoto in situ como estratégia para previsão do rendimento de semente de algodão

Crop harvest scheduling and profits and losses predications require strategies that estimate crop yield. This work aimed to investigate the contribution of phenological variables using path analysis and remote sensing techniques on cotton boll yield and to generate a model using decision trees that help predict cotton boll yield. The sampling field was installed in Chapadão do Céu, in an area of 90 ha. The following phenological variables were evaluated at 30 sample points: plant height at 26, 39, 51, 68, 82, 107, 128, and 185 days after emergence (DAE); number of floral buds at 68, 81, 107, 128, and 185 DAE; number of bolls at 185 DAE; Rededge vegetation index at 23, 35, 53, 91, and 168 DAE; and cotton boll yield. The main variables that can be used to predict cotton boll yield are the number of floral buds (at 107 days after emergence) and the Rededge vegetation index (at 53 and 91 days after emergence). To obtain higher cotton boll yields, the Rededge vegetation index must be greater than 39 at 53 days after emergence, and the plant must present at least 14 floral buds at 107 days after emergence.

O escalonamento de colheitas e a previsão de ganhos e perdas requerem estratégias que estimam a produtividade das culturas. Este trabalho teve como objetivo investigar a contribuição de variáveis fenológicas utilizando técnicas de análise de trilha e sensoriamento remoto sobre a produtividade de algodão em caroço e gerar um modelo utilizando árvores de decisão que ajudam a prever esta variável. O campo de amostragem foi instalado em Chapadão do Céu, em uma área de 90 ha. As seguintes variáveis fenológicas foram avaliadas em 30 pontos amostrais: altura das plantas aos 26, 39, 51, 68, 82, 107, 128 e 185 dias após a emergência (DAE); número de gemas florais aos 68, 81, 107, 128 e 185 DAE; número de cápsulas a 185 DAE; Índice de vegetação Rededge em 23, 35, 53, 91 e 168 DAE; e produção de algodão em caroço. As principais variáveis que podem ser utilizadas para prever a produção de caroço de algodão são o número de gemas florais (aos 107 dias após a emergência) e o índice de vegetação de Rededge (aos 53 e 91 dias após a emergência). Para obter maiores produtividades de algodão, o índice de vegetação de Rededge deve ser superior a 39 aos 53 dias após a emergência e a planta deve apresentar pelo menos 14 gemas florais aos 107 dias após a emergência.

Nitrogen doses in topdressing affect vegetation indices and corn yield / Deses de nitrogênio em cobertura afetam índices de vegetação e produtividade de milho

Nitrogen is the main nutrient required by corn crop, especially in Cerrado soils. Remote sensing techniques can be used to generate additional information now of nitrogen fertilization recommendation. This work investigated the association of plant height and dry matter phenological variables together with NDVI, REDEDGE, SAVI, and IV 760/550 vegetation indices (VIs) with corn grain yield, under different N doses. Sowing occurred in November 2016, at a spacing of 0.45 m between rows and a 60,000 ha-1 plant population. Four N doses (0, 80, 160, and 240 kg of N ha-1) were applied at phenological stage V4. The experimental design consisted of randomized blocks, containing four N doses in topdressing and 16 replications. The active optical sensor Crop Circle ACS-470 was used to obtain the VIs. The NDVI, SAVI, and RE indices have a high positive association with each other and with the variables plant height and dry matter. Polynomial regression equations were adjusted between the variables in response as doses of N. Afterwards, they were estimated as correlations between variables and results expressed through the network of correlations. Finally, a multivariate analysis of canonical variables was performed to understand the interrelationship between the variables and each dose of N applied. NDVI and RE have a positive relationship of moderate magnitude with grain yield in corn crops.

O nitrogênio (N) é o principal nutriente requerido pela cultura do milho, principalmente em solos do Cerrado. Técnicas de sensoriamento remoto podem ser usadas para gerar informações adicionais agora sobre a recomendação de fertilização nitrogenada. Este trabalho investigou a associação de variáveis fenológicas de altura de plantas e matéria seca com os índices de vegetação (IVs) NDVI, REDEDGE, SAVI e IV 760/550 com a produtividade de grãos de milho, sob diferentes doses de N. A semeadura ocorreu em novembro de 2016, com espaçamento de 0,45 m entre linhas e 60.000 ha-1 de população de plantas. Quatro doses de N (0, 80, 160 e 240 kg de N ha-1) foram aplicadas no estádio fenológico V4. O delineamento experimental foi o de blocos casualizados contendo quatro doses de N em cobertura e 16 repetições. O sensor óptico ativo Crop Circle ACS-470 foi usado para obter os IVs. Equações de regressão polinomial foram ajustadas entre as variáveis em resposta como doses de N. Posteriormente, foram estimadas como correlações entre variáveis e resultados expressos através da rede de correlações. Por fim, foi realizada uma análise multivariada de variáveis canônicas para entender a inter-relação entre as variáveis e cada dose de N aplicada. Os índices NDVI, SAVI e RE apresentam alta associação positiva entre si e com as variáveis altura de planta e matéria seca. NDVI e REDEDGE têm uma relação positiva de magnitude moderada com a produtividade de grãos na cultura do milho.

Relationship between cotton productivity and variability of NDVI obtained by landsat images / Relação entre produtividade do algodoeiro e variabilidade do NDVI obtido por imagens do landsat

The availability of satellite images has generated a large number of regional and global studies on vegetation mapping. Such studies have related the growth parameters, nutrient status, physiological responses, and water resources to the yield of agricultural crops or native vegetation. The NDVI (Normalized Difference Vegetation Index) is associated with parameters of growth and yield with readings at several moments of the crop cycle. The objective of this work was to correlate the yield and variability of the NDVI in cotton fields by analyzing Landsat satellite images acquired over nine growing seasons. The study involved the analysis of 101 cotton production fields located in West-Central Brazil. One Landsat image was used during each crop cycle, and the average yield was computed based on total fiber harvested at each field. The fiber yield ranged from 393 to 2,030 kg ha-1, and its correlation with NDVI was 0.37. The coefficient of variation (CV) had a negative correlation with yield, approximating -58.1 kg ha-1 for every one percent increment of the CV. The CV explained the yield variability over the cotton fields more accurately than the average absolute NDVI value.

Desde a disponibilização das imagens de satélites, tem-se gerado grande número de estudos regionais e globais para caracterizar a vegetação. Esses estudos têm relacionado parâmetros de crescimento, nutricionais, fisiológicos, hídricos e produtividade das culturas agrícolas ou vegetação nativa. O NDVI (Normalized Difference Vegetation Index) é associado com parâmetros de crescimento e produtividade do algodoeiro com leituras em vários momentos do ciclo. O objetivo desse trabalho foi relacionar a variabilidade do NDVI em campos de produção de algodão e a produtividade pelo uso de imagens do satélite Landsat ao longo de nove safras. O estudo foi desenvolvido com análise de 101 campos de produção localizados na região central do Brasil. Foi utilizada uma imagem durante o ciclo da cultura e a produtividade média obtida. A produtividade de fibra variou de 393 a 2.030 kg ha-1 e a correlação com NDVI foi de 0,37. O coeficiente de variação teve correlação negativa, com queda na produtividade em -58,1 kg ha-1 ao aumentar um ponto percentual. O coeficiente de variação explicou melhor a variabilidade da produtividade dos talhões que o NDVI médio.

Cotton vegetation indices under diffentent control methods of ramularia leaf spot / Indices de vegetação no algodoeiro sob diferentes fungicidas de controle da mancha da ramulária

This work aimed to correlate treatments using fungicides to different vegetation indices in response to effects caused by ramularia leaf spot (Ramularia areola). The experiment was carried out in the municipality of Chapadão do Sul, state of Mato Grosso do Sul, in the harvest 2016/2017, and consisted of a randomized blocks design, with 17 treatments and four replications. Data were obtained from the Sequoia 4.0 passive sensor and the Green Seeker LT 200 active sensor. From the information recorded by the sensors, nine vegetation indices were generated and compared with the area under the curve of disease progression, plant height, yield, and agronomic efficiency, in 17 different treatments of fungicide products. Treatments responded differently to the product applied. The SAVI index (Soil Adjusted Vegetation Index), obtained from the band in the red spectral range, presented higher correlation to AACPD, agronomic efficiency, and yield. The NDVI index (Normalized Difference Vegetation Index) had a higher correlation to plant height and SR (simple ratio), both using the wavelength in the red spectral range. (AU)

Este trabalho objetivou correlacionar diferentes índices de vegetação em resposta aos efeitos causados pela mancha de ramulária (Ramularia areola) de vários tratamentos com produtos fungicidas. O experimento foi implantado no município de Chapadão do Sul, Estado de Mato Grosso do Sul, no ano agrícola 2016/2017. O delineamento experimental utilizado foi blocos casualizados com 17 tratamentos com quatro repetições. Foram obtidos dados a partir do sensor passivo Sequoia 4.0 e do sensor ativo Green Seeker LT 200. A partir das informações registradas pelos sensores, foram gerados nove índices de vegetação, que foram comparados com a área abaixo da curva de progresso da doença, altura de plantas, produtividade e eficiência agronômica em 17 diferentes tratamentos de produtos de ação fungicida. Os tratamentos responderam de forma distinta em relação ao produto neles aplicados, sendo que os índices SAVI (Soil Adjusted Vegetation Index), obtidos a partir da banda na faixa espectral do Red, apresentaram maior correlação com AACPD, eficiência e produtividade. Já o índice NDVI (Normalized Difference Vegetation Index) obteve maior correlação com a altura de plantas e SR(Simple Ratio),ambosutilizando o comprimento de onda na faixa espectral da banda Red. (AU)